Publications associées à Minimum mean square error

Estimation of Self-Exciting Point Processes from Time-Censored Data

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observatio ...

2023

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Danick Briand, Silvia Demuru, Jaemin Kim, Brince Paul Kunnel, Vincent Gremeaux, Shu Wang

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration ...

2023

Linear Recursive State Estimation of Hybrid and Unbalanced AC/DC Micro-Grids using Synchronized Measurements

Mario Paolone, Willem Lambrichts

In this paper, we present an exact (i. e. non-approximated) and linear measurement model for hybrid AC/DC microgrids for recursive state estimation (SE). More specifically, an exact linear model of a voltage source converter (VSC) is proposed. It relies on ...

New York2023

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Rahul Parhi

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

From Probability Graphical Models to Dynamic Networks — A Bayesian perspective on Smooth Best Estimate of Trajectory with applications in Geodetic Engineering

Laurent Valentin Jospin, Jesse Ray Murray Lahaye

Bayesian statistics is concerned with the integration of new information obtained through observations with prior knowledge, and accordingly, is often related to information theory (Jospin 2022). Recursive Bayesian estimation methods, such as Kalman Filter ...

2023

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

Mathieu Salzmann, Yinlin Hu, Fulin Liu

Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the i ...

Ieee Computer Soc2023

TOPO-Loss for continuity-preserving crack detection using deep learning

Pascal Fua, Katrin Beyer, Bryan German Pantoja Rosero

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

2022

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Minimum mean square error

Estimation of Self-Exciting Point Processes from Time-Censored Data

Timing and spatial selection bias in rapid extreme event attribution

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

Linear Recursive State Estimation of Hybrid and Unbalanced AC/DC Micro-Grids using Synchronized Measurements

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

From Probability Graphical Models to Dynamic Networks — A Bayesian perspective on Smooth Best Estimate of Trajectory with applications in Geodetic Engineering

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

TOPO-Loss for continuity-preserving crack detection using deep learning

Graph Chatbot

Chattez avec Graph Search

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

From Probability Graphical Models to Dynamic Networks — A Bayesian perspective on Smooth Best Estimate of Trajectory with applications in Geodetic Engineering

Timing and spatial selection bias in rapid extreme event attribution

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Estimation of Self-Exciting Point Processes from Time-Censored Data

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

Linear Recursive State Estimation of Hybrid and Unbalanced AC/DC Micro-Grids using Synchronized Measurements

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

TOPO-Loss for continuity-preserving crack detection using deep learning